MS 150 Statistics Calendar and Syllabus • College of Micronesia-FSM • Instructor: Dana Lee Ling
Wk Day Name Date Topic Assess
0 Friday1/12/7 1.1 Overview 1.2 Types of Data 1.3 Critical Thinking • Notes
1 Monday1/15/7 1.4 Design of experiments 1.5 Introduction to [Calc]
Wednesday1/17/7 Barefoot day: Determining your body fat • End add course
Friday1/19/7   Quiz 1
2 Monday1/22/7 2.1 2.2 Frequency Distributions
Wednesday1/24/7 2.3 Visualizing dataGrad apps due
Friday1/26/7   Quiz 2 2.3
3 Monday1/29/7 2.4 Measures of center
Wednesday1/31/7 2.5 Measures of variation • 2.6 Measures of relative standing: z only
Friday2/2/7 Test One answersodsxls
4 Monday2/5/7 9.1 9.2 Correlation Ball drop?
Wednesday2/7/7 9.3 Regression
Friday2/9/7 Early warning Quiz 3
5 Monday2/12/7 3.1 3.2 Fundamentals of probability
Wednesday2/14/7 4.1 4.2 Random variablesMean from distribution
Friday2/16/7   Quiz 4 (3.2)(4.2)
6 Monday2/19/7 Pennies: The shape of randomness
Wednesday2/21/7 5.1 5.2 Standard Normal Distribution
Friday2/23/7 Quiz 5
7 Monday2/26/7 5.3 Applications of Normal Distributions
Wednesday2/28/7  
Friday3/2/7 Midterm5.3 materialanswershtml
8 Monday3/5/7 Review midterm
Wednesday3/7/7 5.4 Sampling Distributions and Estimators
Friday3/9/7 5.5 Central Limit Theorem • Middefs due
9 Monday3/12/7 6.1 6.2 Estimating a population proportion
Wednesday3/14/7 6.3 Estimating a Population mean: σ known
Friday3/16/7 Quiz 6
Wk Day Name Date Topic Assess
10 Monday3/19/7 6.4 Estimating a Population Mean: sx knownSample quiz
Wednesday3/21/7 Hypothesis testing using confidence intervals
Friday3/23/7 LDWWW Quiz 7
11 Monday3/26/7  
Wednesday3/28/7 Test Twot2a
Friday3/30/7 Culture day (Observed)
12 Monday4/2/7Founding Day (Observed)
Wednesday4/4/7 Easter break
Friday4/6/7 Good Friday
13 Monday4/9/7 7.1 7.2 Hypothesis testingCourse selection
Wednesday4/11/7 7.3 Testing a claim about a proportion
Friday4/13/7Quiz 8
14 Monday4/16/7 7.4 Testing a Claim About a Mean: σ known
Wednesday4/18/7 7.5 Testing a Claim About a Mean: σ unknown, sx known
Friday4/20/7 Quiz 9
15 Monday4/23/7 8.1 8.2 Inferences from Two Proportions
Wednesday4/25/7 8.3 Inferences about Two Means: Independent Samples
Friday4/27/7 Quiz 10
16 Monday4/30/7 Barefoot day II
Wednesday5/2/7 8.4 Inferences from matched pairs.
Friday5/4/7 Quiz 11
17 Monday5/7/7 Last day of instruction. Question & Answer session
Wednesday5/9/7 M10 Final at 10:05 found dataodshtml
Friday5/11/7 M09 Final at 8:00 • faraway sakauodsxls
18 Friday5/18/7Graduation

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Course Description: A semester course designed as an introduction to the basic ideas of data presentation, descriptive statistics, linear regression, and inferential statistics including confidence intervals and hypothesis testing. Basic concepts are studied using applications from education, business, social science, and the natural sciences. The course incorporates the use of a computer spreadsheet package for both data analysis and presentation. The course is intended to be taught in a computer laboratory environment.

  1. General Objectives
    Students will be able to:
    1. Calculate basic statistics (define, calculate)
    2. Represent data sets using histograms (define, calculate, estimate, represent)
    3. Solve problems using normal curve and t-statistic distributions including confidence intervals for means and hypothesis testing (define, calculate, solve, interpret)
    4. Determine and interpret p-values (calculate, interpret)
    5. Perform a linear regression and make inferences based on the results (define, calculate, solve, interpret)
  2. Specific Objectives
    Students will be able to: Given one variable data and the use of a calculator or spreadsheet software on a computer
    1. Calculate basic statistics
      1. Distinguish between a population and a sample (define)
      2. Distinguish between a statistic and a parameter (define)
      3. Identify different levels of measurement when presented with nominal, ordinal, interval, and ratio data. (define)
      4. Determine a sample size (calculate)
      5. Determine a sample minimum (calculate)
      6. Determine a sample maximum (calculate)
      7. Calculate a sample range (calculate)
      8. Determine a sample mode (calculate)
      9. Determine a sample median (calculate)
      10. Calculate a sample mean (calculate)
      11. Calculate a sample standard deviation (calculate)
      12. Calculate a sample coefficient of variation (calculate)
    2. Represent data sets using histograms
      1. Calculate a class width given a number of desired classes (calculate)
      2. Determine class upper limits based on the sample minimum and class width (calculate)
      3. Calculate the frequencies (calculate)
      4. Calculate the relative frequencies (probabilities) (calculate)
      5. Create a frequency histogram based on calculated class widths and frequencies (represent)
      6. Create a relative frequency histogram based on calculated class widths and frequencies (represent)
      7. Identify the shape of a distribution as being symmetrical, uniform, bimodal, skewed right, skewed left, or normally symmetric. (define)
      8. Estimate a mean from class upper limits and relative frequencies using the formula ∑x*P(x) here the probability P(x) is the relative frequency. (estimate)
    3. Solve problems using normal curve and t-statistic distributions including confidence intervals for means and hypothesis testing
      1. Discover the normal curve through a course-wide effort involving tossing seven pennies and generating a histogram from the in-class experiment. (develop)
      2. Identify by characteristics normal curves from a set of normal and non-normal graphs of lines. (define)
      3. Determine a point estimate for the population mean based on the sample mean (calculate)
      4. Calculate a z-critical value from a confidence level (calculate)
      5. Calculate a t-critical value from a confidence level and the sample size (calculate)
      6. Calculate an error tolerance from a t-critical, a sample standard deviation, and a sample size. (calculate)
      7. Solve for a confidence interval based on a confidence level, the associated z-critical, a sample standard deviation, and a sample size where the sample size is equal or greater than 30. (solve)
      8. Solve for a confidence interval based on a confidence level, the associated t-critical, a sample standard deviation, and a sample size where the sample size is less than 30. (solve)
      9. Use a confidence interval to determine if the mean of a new sample places the new data within the confidence interval or is statistically significantly different. (interpret)
    4. Determine and interpret p-values
      1. Calculate the two-tailed p-value using a sample mean, sample standard deviation, sample size, and expected population mean to to generate a t-statistic. (calculate)
      2. Infer from a p-value the largest confidence interval for which a change is not significant. (interpret)
    5. Given two variable data and the use of spreadsheet software on a computer
    6. Perform a linear regression and make inferences based on the results
      1. Identify the sign of a least squares line: positive, negative, or zero. (Define)
      2. Calculate the slope of the least squares line. (Calculate)
      3. Calculate the intercept of the least squares line. (Calculate)
      4. Solve for a y value given an x value and the slope and intercept of a least squares line. (Solve)
      5. Solve for a x value given an y value and the slope and intercept of a least squares line. (Solve)
      6. Calculate the correlation coefficient r. (Calculate)
      7. Use a correlation coefficient r to render a judgment as to whether a correlation is perfect, high, moderate, low, or none. (Interpret)
      8. Calculate the coefficient of determination rē. (Calculate)

Course Intentions